I enjoy connecting the components of complex systems in novel ways that benefit society. This drives my fascination with urban energy systems and my long-term goal to use distributed optimization, system modeling, and data analysis to discover strategies for improving global energy sustainability. I pursue that goal by combining operations research and engineering methods to explore problems at the intersection of cities, energy, economics and policy.
I am particularly interested in future urban energy systems where new technologies and control systems enable us to consume electricity with more flexible timing. This flexibility allows millions of autonomous devices to participate in a dynamic electricity market to do things like balance solar and wind output. My current research program studies i) how urban districts adopt and use energy technology, ii) how that technology interacts with the electric grid, and iii) how dynamic prices can optimize that interaction to support a smarter, cleaner power sector.
Impacts of rooftop solar and electric vehicle charging on the electricity demand profiles of U. S. neighborhoods
While rooftop solar and electric vehicle adoption remain relatively small, future residential neighborhoods could see substantial amounts of these technologies, which could significantly impact the demand profile of residential neighborhoods. This project combines simulation and uncertainty analysis to explore how substantial that impact on demand profiles might be.
Large-scale energy efficiency measures as a substitute for building new power plants
If future power grids experience growing demand and retiring power plants, there will be a shortage in generation capacity. This shortage can either be met by building new power plant capacity or by reducing the peak electricity demand. This study assesses the economics of accomplishing peak demand reduction via large-scale investments in residential energy efficiency measures.
Simulating marginal emissions factors for the U. S. power sector
Marginal emissions factors (MEFs) estimate the change in power sector emissions due to a change in electricity demand. This estimation is useful for approximating the emissions impacts of research projects looking at solar development, electric vehicle charging, and other activities. MEFs are traditionally calculated from historical data, which limits their application for studying future scenarios. This project develops a method to calculate MEFs using simulated data, which allows us to estimate the MEFs for future power sector scenarios.